A systematic review of health service interventions to reduce use of unplanned health care in rural areas
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Use of unplanned health care has long been increasing, and not enough is known about which interventions may reduce use. We aimed to review the effectiveness of interventions to reduce the use of unplanned health care by rural populations. METHODS: The method used was systematic review. Scientific databases (Medline, Embase and Central), grey literature and selected references were searched. Study quality and bias was assessed using Cochrane Risk of Bias and modified Newcastle Ottawa Scales. Results were summarized narratively. RESULTS: A total of 2708 scientific articles, reports and other documents were found. After screening, 33 studies met the eligibility criteria, of which eight were randomized controlled trials, 13 were observational studies of unplanned care use before and after new practices were implemented and 12 compared intervention patients with non-randomized control patients. Eight of the 33 studies reported modest statistically significant reductions in unplanned emergency care use while two reported statistically significant increases in unplanned care. Reductions were associated with preventative medicine, telemedicine and targeting chronic illnesses. Cost savings were also reported for some interventions. CONCLUSION: Relatively few studies report on unscheduled medical care by specifically rural populations, and interventions were associated with modest reductions in unplanned care use. Future research should evaluate interventions more robustly and more clearly report the results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".